[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"summary-karpathy-s-pure-python-ai-from-scratch-summary":3,"summaries-facets-categories":70,"summary-related-karpathy-s-pure-python-ai-from-scratch-summary":7020},{"id":4,"title":5,"ai":6,"body":13,"categories":46,"created_at":48,"date_modified":48,"description":40,"extension":49,"faq":48,"featured":50,"kicker_label":48,"meta":51,"navigation":52,"path":53,"published_at":54,"question":48,"scraped_at":48,"seo":55,"sitemap":56,"source_id":57,"source_name":58,"source_type":59,"source_url":60,"stem":61,"tags":62,"thumbnail_url":48,"tldr":67,"tweet":48,"unknown_tags":68,"__hash__":69},"summaries\u002Fsummaries\u002Fkarpathy-s-pure-python-ai-from-scratch-summary.md","Karpathy's Pure Python AI From Scratch",{"provider":7,"model":8,"input_tokens":9,"output_tokens":10,"processing_time_ms":11,"cost_usd":12},"openrouter","x-ai\u002Fgrok-4.1-fast",4820,1448,12742,0.0012176,{"type":14,"value":15,"toc":39},"minimark",[16,21,25,29,32,36],[17,18,20],"h2",{"id":19},"minimal-code-for-core-ai-models","Minimal Code for Core AI Models",[22,23,24],"p",{},"Train and run a full GPT in just 200 lines of dependency-free Python, covering tokenization, model architecture, training loop, and sampling—proving LLMs are accessible without frameworks. Similarly, implement deep RL to master Atari Pong from raw pixels using policy gradients, weighing pros (sample efficiency) against cons (high variance). Character-level RNNs generate poetry, LaTeX, and code; analyze gradients to spot future directions like better optimization. Fool ImageNet classifiers with tiny perturbations, showing even linear models (not just convnets) break easily, challenging robustness claims.",[17,26,28],{"id":27},"historical-benchmarks-and-progress","Historical Benchmarks and Progress",[22,30,31],{},"Revisit LeCun's 1989 backprop-trained neural net—the first real-world end-to-end DL app—then upgrade it with 33 years of advances (e.g., modern optimizers, architectures) to quantify progress; preview how 2022 DL will age by 2055. Humans hit 6.7% error@5 on ImageNet vs. top convnets, but manual CIFAR-10 labeling reveals human baselines aren't unbeatable. Early CV state (2012) lags far behind human vision, tempering AI hype.",[17,33,35],{"id":34},"practical-training-and-experiments","Practical Training and Experiments",[22,37,38],{},"Follow a battle-tested recipe for neural nets: batch size 0.2-10% of GPU memory, weak regularization first, then strengthen; cosine anneal LR over 1M steps. Scrape 2M selfies to train convnets classifying good\u002Fbad #selfies, visualizing what networks 'think'. Track productivity via window\u002Fkeystroke logging on Ubuntu\u002FOSX, generating HTML viz for insights. Biohacking basics: tweak energy metabolism via experiments. PhD survival: navigate academia with tips on focus, advising.",{"title":40,"searchDepth":41,"depth":41,"links":42},"",2,[43,44,45],{"id":19,"depth":41,"text":20},{"id":27,"depth":41,"text":28},{"id":34,"depth":41,"text":35},[47],"AI & LLMs",null,"md",false,{},true,"\u002Fsummaries\u002Fkarpathy-s-pure-python-ai-from-scratch-summary","2026-04-08 21:21:19",{"title":5,"description":40},{"loc":53},"2ff230eac68aac35","Andrej Karpathy Blog","article","https:\u002F\u002Funknown","summaries\u002Fkarpathy-s-pure-python-ai-from-scratch-summary",[63,64,65,66],"python","llm","deep-learning","machine-learning","Andrej Karpathy distills neural nets, LLMs, RL, and Bitcoin into 200-500 line pure Python scripts—no deps needed—to teach core mechanics 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Fixes Muon's Neuron Death in Tall MLPs",{"provider":7,"model":8,"input_tokens":7025,"output_tokens":7026,"processing_time_ms":7027,"cost_usd":7028},7761,2013,23604,0.00253605,{"type":14,"value":7030,"toc":7058},[7031,7035,7038,7041,7045,7048,7051,7055],[17,7032,7034],{"id":7033},"muons-orthogonal-updates-cause-neuron-death-in-tall-matrices","Muon's Orthogonal Updates Cause Neuron Death in Tall Matrices",[22,7036,7037],{},"Muon computes the polar factor UVᵀ of gradient matrix G (via thin SVD) for semi-orthogonal weight updates W ← W - η UVᵀ, enabling fast convergence on nanoGPT speedrun benchmarks over AdamW. In tall matrices like SwiGLU MLP up-projections (more rows n than columns m), row-norm anisotropy emerges: impossible for perfectly orthogonal matrices to have uniform row norms of 1, so some rows get massive updates while others starve. By training step 500, >1\u002F4 neurons die permanently, starving downstream layers and compounding inefficiency. Leverage scores (squared row norms of U) become highly anisotropic, amplifying the death spiral.",[22,7039,7040],{},"NorMuon patches this with inverse RMS row normalization to unit norm, boosting performance but sacrificing polar factor precision. U-NorMuon refines to target norm √(n\u002Fm) for column-orthogonal tall matrices, eliminating death and stabilizing gradients even in untouched layers like down-projections—at 340M scale, it outperforms Muon\u002FNorMuon with isotropic leverage.",[17,7042,7044],{"id":7043},"aurora-solves-joint-constraints-for-precise-uniform-updates","Aurora Solves Joint Constraints for Precise, Uniform Updates",[22,7046,7047],{},"Aurora reformulates as steepest descent maximizing Tr(GᵀU) under dual constraints: UᵀU = Iₙ (left semi-orthogonality) and ||U_||₂ = √(m\u002Fn) ∀i (uniform row leverage). This forces all singular values of U to 1, achieving perfect orthogonality without trade-offs—unlike NorMuon's post-hoc normalization.",[22,7049,7050],{},"Implement as drop-in Muon replacement: Riemannian Aurora (gradient projection on Stiefel\u002Fequal-leverage manifold) or vanilla Aurora (simpler). For wide\u002Fsquare matrices, orthogonality implies uniformity, so unchanged. Open-source code supports scale; adds only 6% compute vs. Muon.",[17,7052,7054],{"id":7053},"sota-results-scale-with-mlp-width","SOTA Results Scale with MLP Width",[22,7056,7057],{},"At 1.1B parameters, Aurora trains 100x data-efficient model on open internet data, beating larger models on HellaSwag. Tops modded-nanoGPT speedrun (prior SOTA: NorMuon). Gains grow with MLP expansion (wider = taller matrices = more anisotropy risk), confirming hypothesis. Use for GPT-style training to avoid silent capacity loss.",{"title":40,"searchDepth":41,"depth":41,"links":7059},[7060,7061,7062],{"id":7033,"depth":41,"text":7034},{"id":7043,"depth":41,"text":7044},{"id":7053,"depth":41,"text":7054},[47],{"content_references":7065,"triage":7076},[7066,7072],{"type":7067,"title":7068,"author":7069,"url":7070,"context":7071},"paper","Aurora","Tilde Research","https:\u002F\u002Fblog.tilderesearch.com\u002Fblog\u002Faurora","recommended",{"type":7073,"title":7074,"url":7075,"context":7071},"tool","aurora-release","https:\u002F\u002Fgithub.com\u002Ftilde-research\u002Faurora-release",{"relevance":7077,"novelty":7078,"quality":7078,"actionability":41,"composite":7079,"reasoning":7080},3,4,3.25,"Category: AI & LLMs. The article discusses a new optimizer, Aurora, that addresses a specific technical problem in deep learning models, which is relevant to AI engineering. However, while it presents novel insights into the optimizer's mechanics and performance, it lacks practical guidance for implementation that the target audience could directly act upon.","\u002Fsummaries\u002Fdcb9afa6c7f04fd4-aurora-fixes-muon-s-neuron-death-in-tall-mlps-summary","2026-05-12 08:07:28","2026-05-12 15:01:25",{"title":7023,"description":40},{"loc":7081},"dcb9afa6c7f04fd4","MarkTechPost","https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F05\u002F12\u002Ftilde-research-introduces-aurora-a-leverage-aware-optimizer-that-fixes-a-hidden-neuron-death-problem-in-muon\u002F","summaries\u002Fdcb9afa6c7f04fd4-aurora-fixes-muon-s-neuron-death-in-tall-mlps-summary",[66,64,65],"Aurora optimizer eliminates >25% neuron death in Muon's tall matrices by jointly enforcing left semi-orthogonality and uniform row norms √(n\u002Fm), delivering SOTA on nanoGPT speedrun with 6% compute overhead.",[],"LbY7EBmj0SNTdCqYLDJeH1MTGWukIbA19aMUaOvqp7Y",{"id":7095,"title":7096,"ai":7097,"body":7102,"categories":7158,"created_at":48,"date_modified":48,"description":40,"extension":49,"faq":48,"featured":50,"kicker_label":48,"meta":7159,"navigation":52,"path":7180,"published_at":7181,"question":48,"scraped_at":7182,"seo":7183,"sitemap":7184,"source_id":7185,"source_name":7087,"source_type":59,"source_url":7186,"stem":7187,"tags":7188,"thumbnail_url":48,"tldr":7189,"tweet":48,"unknown_tags":7190,"__hash__":7191},"summaries\u002Fsummaries\u002F79f82c07ea7441fe-trl-code-guide-sft-to-grpo-llm-alignment-on-t4-gpu-summary.md","TRL Code Guide: SFT to GRPO LLM Alignment on T4 GPU",{"provider":7,"model":8,"input_tokens":7098,"output_tokens":7099,"processing_time_ms":7100,"cost_usd":7101},9458,2615,35753,0.00269195,{"type":14,"value":7103,"toc":7152},[7104,7108,7116,7120,7130,7134,7140,7144],[17,7105,7107],{"id":7106},"lora-and-trl-setup-enables-post-training-on-limited-hardware","LoRA and TRL Setup Enables Post-Training on Limited Hardware",[22,7109,7110,7111,7115],{},"Use LoRA (r=8, alpha=16, dropout=0.05, targets=",[7112,7113,7114],"span",{},"'q_proj','k_proj','v_proj','o_proj'",") with TRL trainers to adapt Qwen\u002FQwen2.5-0.5B-Instruct on T4 GPU (16GB). Common args across stages: num_train_epochs=1, gradient_checkpointing=True, bf16 if supported else fp16, logging_steps=10, report_to=\"none\", save_strategy=\"no\". Install stack: torchao>=0.16, trl>=0.20, transformers>=4.45, peft>=0.13, bitsandbytes. Helpers like chat_generate apply chat template, generate with temp=0.7\u002Ftop_p=0.9. Cleanup VRAM with gc.collect() + torch.cuda.empty_cache() between stages to fit in Colab.",[17,7117,7119],{"id":7118},"sft-and-rm-build-imitation-and-reward-signals","SFT and RM Build Imitation and Reward Signals",[22,7121,7122,7123,7126,7127,7129],{},"For Supervised Fine-Tuning, load trl-lib\u002FCapybara (train",[7112,7124,7125],{},":300","), use SFTConfig(per_device_train_batch_size=2, gradient_accumulation_steps=4, learning_rate=2e-4, max_length=768). Trainer imitates high-quality chat responses; post-train inference on \"Explain bias-variance tradeoff in two sentences\" yields coherent output. Reward Modeling on trl-lib\u002Fultrafeedback_binarized (train",[7112,7128,7125],{},") uses RewardConfig(batch_size=2, accum_steps=2, lr=1e-4, max_length=512), LoRA task_type=\"SEQ_CLS\". Trains to score chosen vs. rejected pairs, producing a preference-based reward without explicit RL.",[17,7131,7133],{"id":7132},"dpo-skips-rm-for-direct-preference-alignment","DPO Skips RM for Direct Preference Alignment",[22,7135,7136,7137,7139],{},"DPOTrainer on same ultrafeedback_binarized",[7112,7138,7125],{}," simplifies via implicit rewards: DPOConfig(batch_size=1, accum_steps=4, lr=5e-6, beta=0.1, max_length=512, max_prompt_length=256). Beta controls KL-divergence from reference policy, preventing mode collapse. Optimizes policy to prefer chosen over rejected responses directly, reducing steps vs. traditional RM+PPO.",[17,7141,7143],{"id":7142},"grpo-uses-custom-rewards-to-sharpen-reasoning","GRPO Uses Custom Rewards to Sharpen Reasoning",[22,7145,7146,7147,7151],{},"GRPOTrainer generates num_generations=4 completions per prompt (max_prompt_length=128, max_completion_length=96, max_steps=15), ranks via reward_funcs. Custom dataset: 200 synthetic math problems (e.g., \"Solve 17 + 28 =\", gold=eval). Rewards: correctness_reward (1.0 if last extracted number matches gold else 0), brevity_reward (max(0,1-len(c)\u002F200)",[7148,7149,7150],"em",{},"0.2). GRPOConfig(lr=1e-5, batch=2, accum=2). Inference on \"17+28?\", \"9","7?\", \"100-47?\" produces accurate, concise answers like final numbers, improving verifiable task performance over base.",{"title":40,"searchDepth":41,"depth":41,"links":7153},[7154,7155,7156,7157],{"id":7106,"depth":41,"text":7107},{"id":7118,"depth":41,"text":7119},{"id":7132,"depth":41,"text":7133},{"id":7142,"depth":41,"text":7143},[47],{"content_references":7160,"triage":7176},[7161,7165,7168,7170,7172],{"type":7073,"title":7162,"url":7163,"context":7164},"TRL","https:\u002F\u002Fgithub.com\u002Fhuggingface\u002Ftrl","mentioned",{"type":7166,"title":7167,"context":7164},"dataset","trl-lib\u002FCapybara",{"type":7166,"title":7169,"context":7164},"trl-lib\u002Fultrafeedback_binarized",{"type":7073,"title":7171,"context":7164},"Qwen\u002FQwen2.5-0.5B-Instruct",{"type":7173,"title":7174,"url":7175,"context":7071},"other","trl_llm_post_training_sft_dpo_grpo_marktechpost.py","https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Agents-Projects-Tutorials\u002Fblob\u002Fmain\u002FLLM%20Projects\u002Ftrl_llm_post_training_sft_dpo_grpo_marktechpost.py",{"relevance":7177,"novelty":7078,"quality":7078,"actionability":7177,"composite":7178,"reasoning":7179},5,4.55,"Category: AI & LLMs. The article provides a detailed guide on using TRL and LoRA for LLM post-training, addressing practical applications for developers looking to implement AI features. It includes specific configurations and techniques that can be directly applied in production, making it highly actionable.","\u002Fsummaries\u002F79f82c07ea7441fe-trl-code-guide-sft-to-grpo-llm-alignment-on-t4-gpu-summary","2026-05-01 20:52:08","2026-05-03 17:01:49",{"title":7096,"description":40},{"loc":7180},"79f82c07ea7441fe","https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F05\u002F01\u002Fa-coding-guide-on-llm-post-training-with-trl-from-supervised-fine-tuning-to-dpo-and-grpo-reasoning\u002F","summaries\u002F79f82c07ea7441fe-trl-code-guide-sft-to-grpo-llm-alignment-on-t4-gpu-summary",[64,63,66],"Train Qwen2.5-0.5B via SFT, RM, DPO, GRPO using TRL+LoRA on Colab T4: configs include r=8 LoRA, 300-sample datasets, epochs=1, small batches\u002Faccum for memory efficiency, custom math rewards boost reasoning.",[],"py8Fe1-Noi99CHywKy61Q363dqRBmUxl6tZ9TDJOp3E",{"id":7193,"title":7194,"ai":7195,"body":7200,"categories":7228,"created_at":48,"date_modified":48,"description":40,"extension":49,"faq":48,"featured":50,"kicker_label":48,"meta":7229,"navigation":52,"path":7242,"published_at":7243,"question":48,"scraped_at":7244,"seo":7245,"sitemap":7246,"source_id":7247,"source_name":7248,"source_type":59,"source_url":7249,"stem":7250,"tags":7251,"thumbnail_url":48,"tldr":7252,"tweet":48,"unknown_tags":7253,"__hash__":7254},"summaries\u002Fsummaries\u002F36eeccb45fcfb891-sentences-define-word-meanings-via-self-attention-summary.md","Sentences Define Word Meanings via Self-Attention",{"provider":7,"model":8,"input_tokens":7196,"output_tokens":7197,"processing_time_ms":7198,"cost_usd":7199},6053,1614,12893,0.00199495,{"type":14,"value":7201,"toc":7223},[7202,7206,7209,7213,7216,7220],[17,7203,7205],{"id":7204},"sequential-architectures-failed-to-capture-full-context","Sequential Architectures Failed to Capture Full Context",[22,7207,7208],{},"Pre-Transformer models processed language word-by-word, causing inevitable information loss. RNNs from the late 1980s suffered vanishing gradients, where early words faded by sentence end—like a goldfish memory in long sequences. LSTMs (1997) added forget, input, and output gates to selectively retain info, powering Google Translate and Gmail Smart Reply, but tripled parameters and computation costs. GRUs (2014) merged gates for half the compute with similar performance. Seq2Seq models also compressed entire inputs into fixed-size vectors for tasks like translation, creating bottlenecks where long inputs lost early details—short sentences worked, but nuance blurred in longer ones. All shared a core limit: sequential processing prevented parallel handling, capping scalability for documents beyond hundreds of words.",[17,7210,7212],{"id":7211},"self-attention-enables-sentence-level-meaning-resolution","Self-Attention Enables Sentence-Level Meaning Resolution",[22,7214,7215],{},"The 2017 'Attention Is All You Need' paper by eight Google engineers introduced Transformers, ditching RNNs\u002FLSTMs\u002FGRUs for parallel processing via self-attention. Every word simultaneously queries every other: 'How relevant are you to me?' This dynamically adjusts representations based on full context. For 'I bought apple to eat,' 'apple' weights 'eat' and 'bought' toward fruit; in 'I bought Apple stock to sell,' it shifts to company. Ambiguous pronouns resolve naturally, as in 'The trophy did not fit in the suitcase because it was too big'—full sentence clarifies 'it' as suitcase. Mimicking human reading (whole-sentence intake), this eliminates fixed meanings for words like 'bank' (river\u002Fmoney) or 'apple' (fruit\u002Fcompany), deriving them from sentence signals. Original Transformer trained in 3.5 days on eight GPUs, beating benchmarks.",[17,7217,7219],{"id":7218},"transformers-scale-to-power-all-modern-llms","Transformers Scale to Power All Modern LLMs",[22,7221,7222],{},"OpenAI's GPT series built directly on this: GPT-1 (117M parameters) to GPT-4 (>1T estimated), all using self-attention for billions of relevance computations per second. Every chatbot (ChatGPT, Claude), autocomplete, and LLM since runs this core operation, replacing fading memories and bottlenecks. Words lack inherent meaning—sentences solve them as variables, a truth machines grasped only after 30 years and one six-page paper.",{"title":40,"searchDepth":41,"depth":41,"links":7224},[7225,7226,7227],{"id":7204,"depth":41,"text":7205},{"id":7211,"depth":41,"text":7212},{"id":7218,"depth":41,"text":7219},[47],{"content_references":7230,"triage":7239},[7231,7236],{"type":7067,"title":7232,"author":7233,"publisher":7234,"context":7235},"Attention Is All You Need","Eight engineers at Google","Google","cited",{"type":7073,"title":7237,"url":7238,"context":7071},"Self-Attention Interactive Walkthrough","https:\u002F\u002Fnursnaaz.github.io",{"relevance":7077,"novelty":7077,"quality":7078,"actionability":41,"composite":7240,"reasoning":7241},3.05,"Category: AI & LLMs. The article discusses the evolution of language models and the significance of self-attention in Transformers, which is relevant to AI-powered product builders. However, it lacks practical applications or frameworks that the audience could directly implement.","\u002Fsummaries\u002F36eeccb45fcfb891-sentences-define-word-meanings-via-self-attention-summary","2026-04-21 00:30:43","2026-04-21 15:26:03",{"title":7194,"description":40},{"loc":7242},"36eeccb45fcfb891","Generative AI","https:\u002F\u002Fgenerativeai.pub\u002Fwords-dont-have-meaning-sentences-do-ef5b7745eac2?source=rss----440100e76000---4","summaries\u002F36eeccb45fcfb891-sentences-define-word-meanings-via-self-attention-summary",[64,66,65],"Transformers ended 30 years of sequential processing flaws by using self-attention, where every word weighs relevance from the entire sentence context, powering GPT and all modern LLMs.",[],"oCj4Ws9wcBmSiLHpHgFwkn32mNINxj5NzpYDjicxhYg",{"id":7256,"title":7257,"ai":7258,"body":7263,"categories":7291,"created_at":48,"date_modified":48,"description":40,"extension":49,"faq":48,"featured":50,"kicker_label":48,"meta":7292,"navigation":52,"path":7300,"published_at":7301,"question":48,"scraped_at":7302,"seo":7303,"sitemap":7304,"source_id":7305,"source_name":7306,"source_type":59,"source_url":7307,"stem":7308,"tags":7309,"thumbnail_url":48,"tldr":7310,"tweet":48,"unknown_tags":7311,"__hash__":7312},"summaries\u002Fsummaries\u002Fd184bc13d59ed16f-53x-ai-efficiency-via-model-distillation-by-2025-summary.md","53x AI Efficiency via Model Distillation by 2025",{"provider":7,"model":8,"input_tokens":7259,"output_tokens":7260,"processing_time_ms":7261,"cost_usd":7262},3863,1216,6667,0.00135795,{"type":14,"value":7264,"toc":7286},[7265,7269,7272,7276,7279,7283],[17,7266,7268],{"id":7267},"core-technique-student-mimics-teachers-nuances","Core Technique: Student Mimics Teacher's Nuances",[22,7270,7271],{},"Model distillation compresses large AI models into smaller ones by having a 'student' model learn directly from a 'teacher' model's soft outputs—probability distributions over answers—rather than hard final labels. This captures subtle knowledge like confidence levels that label-only training misses, enabling deployment on limited hardware. In practice, apply it when large models are accurate but too slow or resource-heavy: the student slashes model size and boosts inference speed dramatically without major accuracy drops.",[17,7273,7275],{"id":7274},"proven-efficiency-gains-and-real-world-impact","Proven Efficiency Gains and Real-World Impact",[22,7277,7278],{},"Distillation delivers 53x overall efficiency improvements by 2025 across speed, cost, size, and energy use, making AI greener and cheaper for production. For instance, it turns impossible edge deployments into reality, as the author experienced in a project where mimicking a large model's behavior overcame hardware constraints. Smaller models run faster and cheaper while retaining complex capabilities, ideal for real-world apps over bulky originals.",[17,7280,7282],{"id":7281},"evolution-from-2015-pioneer-to-modern-power","Evolution from 2015 Pioneer to Modern Power",[22,7284,7285],{},"Geoffrey Hinton introduced distillation in his 2015 paper, starting with basic mimicry. It has since advanced to embed reasoning and instruction-following into compact models. By 2025, expect widespread adoption for massive gains, evolving beyond simple compression to transfer advanced AI behaviors efficiently. This thin intro highlights the method's maturity but cuts off before deeper 2025 specifics or code examples.",{"title":40,"searchDepth":41,"depth":41,"links":7287},[7288,7289,7290],{"id":7267,"depth":41,"text":7268},{"id":7274,"depth":41,"text":7275},{"id":7281,"depth":41,"text":7282},[47],{"content_references":7293,"triage":7297},[7294],{"type":7067,"title":7295,"author":7296,"context":7235},"Geoffrey Hinton’s pioneering 2015 paper","Geoffrey Hinton",{"relevance":7078,"novelty":7077,"quality":7078,"actionability":7077,"composite":7298,"reasoning":7299},3.6,"Category: AI & LLMs. The article discusses model distillation, a relevant technique for improving AI efficiency, which addresses the audience's pain point of deploying AI models in resource-constrained environments. It provides a concrete example of efficiency gains but lacks detailed actionable steps for implementation.","\u002Fsummaries\u002Fd184bc13d59ed16f-53x-ai-efficiency-via-model-distillation-by-2025-summary","2026-04-17 03:31:01","2026-04-19 01:22:22",{"title":7257,"description":40},{"loc":7300},"d184bc13d59ed16f","AI Simplified in Plain English","https:\u002F\u002Fmedium.com\u002Fai-simplified-in-plain-english\u002Fdiscover-the-hidden-power-of-model-distillation-38f40d343c85?source=rss----f37ab7d4e76b---4","summaries\u002Fd184bc13d59ed16f-53x-ai-efficiency-via-model-distillation-by-2025-summary",[66,65,64],"Train small 'student' models on large 'teacher' models' soft probabilities—not just labels—to match performance while slashing size, speed, and costs by 53x by 2025.",[],"ECAF7XlAWzBa4F6NguTIJJt3uE7W33V32wLDIbjUNnU"]